{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a Deep Neural Network For Predicting a Stock's Closing Price\n",
    "\n",
    "This notebook is a guide on training a deep neural network (dnn) to predict a stock's closing price from an algorithm's trading history. The [Analysis Engine](https://github.com/jay-johnson/stock-analysis-engine) makes it easy to roll custom AI training datasets from minute-by-minute, intraday algorithm backtests. Each algorithm backtest automatically publishes its trading history to S3 (Minio or AWS). These trading histories are compressed [pandas DataFrames](http://pandas.pydata.org/pandas-docs/stable/reference/frame.html) where the algorithm's indicator decisions are stored as columns withnin this DataFrame. These columns become training features for a dnn to learn (hopefully)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How it works\n",
    "\n",
    "#### Before Running the Notebook\n",
    "\n",
    "1. Run through [Getting Started with the Analysis Engine](https://github.com/AlgoTraders/stock-analysis-engine#getting-started)\n",
    "\n",
    "1. [Publish a Trading History from an Algorithm's Backtest to S3](https://github.com/AlgoTraders/stock-analysis-engine#run-a-local-backtest-and-publish-algorithm-trading-history-to-s3)\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "1. Load the Runtime\n",
    "\n",
    "1. Set Algorithm's S3 Bucket and Key Values as needed\n",
    "\n",
    "1. Load the Trading History Dataset from S3\n",
    "\n",
    "1. Prepare Training Dataset from Trading History\n",
    "   \n",
    "   (**This version is only using High + Low + Open without scaler normalization - stay tuned!**)\n",
    "\n",
    "1. Create the Test and Train Dataset Splits\n",
    "\n",
    "1. Build and Compile a KerasRegressor using Keras + Tensorflow + Scikit-learn\n",
    "\n",
    "1. Fit the DNN (train)\n",
    "\n",
    "1. Determine Error (MSE + MAE + MAPE + CP)\n",
    "\n",
    "1. Run DNN Predictions\n",
    "\n",
    "1. Plot Historical Close vs DNN's Predicted Close\n",
    "\n",
    "The current notebook uses Keras and Tensorflow to build a KerasRegressor.\n",
    "\n",
    "This regressor is created using a custom layer configuration (json dictionary) that can support Dense and Dropout Keras layer types. After creation it will    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting - 2019-01-27 02:22:00.785266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imports done - 2019-01-27 02:22:03.685627\n",
      "ready - 2019-01-27 02:22:03.688396\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(f'starting - {datetime.datetime.now()}')\n",
    "\n",
    "import copy\n",
    "import numpy as np\n",
    "import numpy.random as np_random\n",
    "import pandas as pd\n",
    "import pandas.api.types as pandas_types\n",
    "import sklearn.model_selection as tt_split\n",
    "import keras.wrappers.scikit_learn as keras_scikit\n",
    "import tensorflow as tf\n",
    "import analysis_engine.consts as ae_consts\n",
    "import analysis_engine.load_history_dataset as load_history\n",
    "import analysis_engine.ai.build_regression_dnn as build_dnn\n",
    "import analysis_engine.plot_trading_history as plot_trading_history\n",
    "\n",
    "print(f'imports done - {datetime.datetime.now()}')\n",
    "\n",
    "# ensure reproducible results tip from:\n",
    "# https://machinelearningmastery.com/reproducible-results-neural-networks-keras/\n",
    "np_random.seed(1)\n",
    "\n",
    "print(f'ready - {datetime.datetime.now()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Algorithm's S3 Bucket and Key Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change these to your values:\n",
    "s3_bucket = (\n",
    "    f'algohistory')\n",
    "s3_key = (\n",
    "    f'algo_training_SPY.json')\n",
    "\n",
    "s3_access_key = ae_consts.S3_ACCESS_KEY\n",
    "s3_secret_key = ae_consts.S3_SECRET_KEY\n",
    "s3_region_name = ae_consts.S3_REGION_NAME\n",
    "s3_address = ae_consts.S3_ADDRESS\n",
    "s3_secure = ae_consts.S3_SECURE\n",
    "compress = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Trading History Dataset from S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded trading history from s3://algohistory/algo_training_SPY.json\n"
     ]
    }
   ],
   "source": [
    "load_res = load_history.load_history_dataset(\n",
    "    s3_key=s3_key,\n",
    "    s3_address=s3_address,\n",
    "    s3_bucket=s3_bucket,\n",
    "    s3_access_key=s3_access_key,\n",
    "    s3_secret_key=s3_secret_key,\n",
    "    s3_region_name=s3_region_name,\n",
    "    s3_secure=s3_secure,\n",
    "    compress=compress)\n",
    "\n",
    "ticker = None\n",
    "if 'tickers' not in load_res:\n",
    "    print(\n",
    "        f'unable to load trading history from s3://{s3_address}/{s3_bucket}/{s3_key}')\n",
    "else:\n",
    "    tickers = load_res.get(\n",
    "        'tickers',\n",
    "        [\n",
    "            'SPY',\n",
    "        ])\n",
    "    ticker = tickers[0]\n",
    "    print(\n",
    "        f'loaded trading history from s3://{s3_bucket}/{s3_key}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Training Dataset from Trading History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prepared training data from history algohistory@algo_training_SPY.json rows=395 dates: 2019-01-18 16:00:00 to 2019-01-25 15:59:00\n"
     ]
    }
   ],
   "source": [
    "df = load_res[ticker]\n",
    "df['date'] = pd.to_datetime(\n",
    "    df['date'])\n",
    "df['minute'] = pd.to_datetime(\n",
    "    df['minute'])\n",
    "ticker = df['ticker'].iloc[0]\n",
    "first_date = df['date'].iloc[0]\n",
    "end_date = df['date'].iloc[-1]\n",
    "\n",
    "if 'minute' in df:\n",
    "    found_valid_minute = df['minute'].iloc[0]\n",
    "    if found_valid_minute:\n",
    "        first_date = df['minute'].iloc[0]\n",
    "        end_date = df['minute'].iloc[-1]\n",
    "\n",
    "num_rows = len(df.index)\n",
    "\n",
    "print(\n",
    "    f'prepared training data from history {s3_bucket}@{s3_key} '\n",
    "    f'rows={num_rows} '\n",
    "    f'dates: {first_date} to {end_date}')\n",
    "\n",
    "\"\"\"\n",
    "for scaler-normalized datasets this will\n",
    "compile numeric columns and ignore string/non-numeric\n",
    "columns as training and test feature columns\n",
    "\"\"\"\n",
    "predict_feature = 'close'\n",
    "use_all_features = False \n",
    "all_features = []\n",
    "train_features = []\n",
    "if use_all_features:\n",
    "    for c in df.columns.values:\n",
    "        if pandas.api.types.is_numeric_dtype(df[c]):\n",
    "            if c != predict_feature:\n",
    "                train_features.append(c)\n",
    "            all_features.append(c)\n",
    "else:\n",
    "    # easier to debug if there's less features to train\n",
    "    train_features = [\n",
    "        'high',\n",
    "        'low',\n",
    "        'open'\n",
    "    ]\n",
    "    all_features = [\n",
    "        'close'\n",
    "    ] + train_features\n",
    "\n",
    "num_features = len(train_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Deep Neural Network Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_config = {\n",
    "    'layers': [\n",
    "        {\n",
    "            'num_neurons': 150,\n",
    "            'init': 'uniform',\n",
    "            'activation': 'relu'\n",
    "        },\n",
    "        {\n",
    "            'num_neurons': 100,\n",
    "            'init': 'uniform',\n",
    "            'activation': 'relu'\n",
    "        },\n",
    "        {\n",
    "            'num_neurons': 50,\n",
    "            'init': 'uniform',\n",
    "            'activation': 'relu'\n",
    "        },\n",
    "        {\n",
    "            'num_neurons': 1,\n",
    "            'init': 'uniform',\n",
    "            'activation': 'relu'\n",
    "        }\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Keras Compile Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "compile_config = {\n",
    "    'loss': 'mse',\n",
    "    'optimizer': 'adam',\n",
    "    'metrics': [\n",
    "        'accuracy',\n",
    "        'mse',\n",
    "        'mae',\n",
    "        'mape',\n",
    "        'cosine'\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Prediction Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_feature = 'close'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Train and Test DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building train and test dfs\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'building train and test dfs')\n",
    "\n",
    "converted_df = df[all_features].dropna().astype(\n",
    "    'float32')\n",
    "train_df = converted_df[train_features]\n",
    "test_df = converted_df[[predict_feature]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and Compile a KerasRegressor using Keras + Tensorflow + Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_epochs = 20\n",
    "use_batch_size = 10\n",
    "use_test_size = 0.1\n",
    "use_random_state = 1\n",
    "use_seed = 7  # change this to random in prod\n",
    "use_shuffle = False\n",
    "model_verbose = True\n",
    "fit_verbose = True\n",
    "\n",
    "def set_model():\n",
    "    return build_dnn.build_regression_dnn(\n",
    "        num_features=num_features,\n",
    "        compile_config=compile_config,\n",
    "        model_config=dnn_config)\n",
    "\n",
    "estimator = keras_scikit.KerasRegressor(\n",
    "    build_fn=set_model,\n",
    "    epochs=use_epochs,\n",
    "    batch_size=use_batch_size,\n",
    "    verbose=model_verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Test and Train Dataset Splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "split breakdown - x_train=354 x_test=40 y_train=354 y_test=40\n"
     ]
    }
   ],
   "source": [
    "(x_train,\n",
    " x_test,\n",
    " y_train,\n",
    " y_test) = tt_split.train_test_split(\n",
    "     train_df,\n",
    "     test_df,\n",
    "     test_size=use_test_size,\n",
    "     random_state=use_random_state)\n",
    "\n",
    "print(\n",
    "    f'split breakdown - '\n",
    "    f'x_train={len(x_train)} '\n",
    "    f'x_test={len(x_test)} '\n",
    "    f'y_train={len(y_train)} '\n",
    "    f'y_test={len(y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the DNN (train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 354 samples, validate on 354 samples\n",
      "Epoch 1/20\n",
      "354/354 [==============================] - ETA: 10s - loss: 70804.8281 - acc: 0.0000e+00 - mean_squared_error: 70804.8281 - mean_absolute_error: 266.0917 - mean_absolute_percentage_error: 99.9638 - cosine_proximity: -1.000 - ETA: 0s - loss: 67224.0867 - acc: 0.0050 - mean_squared_error: 67224.0867 - mean_absolute_error: 258.5610 - mean_absolute_percentage_error: 89638.2817 - cosine_proximity: -0.9950  - 0s 1ms/step - loss: 54972.0017 - acc: 0.0113 - mean_squared_error: 54972.0017 - mean_absolute_error: 229.6648 - mean_absolute_percentage_error: 1336357.7580 - cosine_proximity: -0.9887 - val_loss: 8709.5013 - val_acc: 0.0113 - val_mean_squared_error: 8709.5013 - val_mean_absolute_error: 92.7989 - val_mean_absolute_percentage_error: 3012164.1780 - val_cosine_proximity: -0.9887\n",
      "Epoch 2/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 8819.7578 - acc: 0.0000e+00 - mean_squared_error: 8819.7578 - mean_absolute_error: 93.9135 - mean_absolute_percentage_error: 35.2809 - cosine_proximity: -1.000 - ETA: 0s - loss: 2516.8224 - acc: 0.0045 - mean_squared_error: 2516.8224 - mean_absolute_error: 42.5028 - mean_absolute_percentage_error: 2200508.1210 - cosine_proximity: -0.99 - 0s 347us/step - loss: 1807.9665 - acc: 0.0113 - mean_squared_error: 1807.9665 - mean_absolute_error: 35.3929 - mean_absolute_percentage_error: 4490692.0310 - cosine_proximity: -0.9887 - val_loss: 0.3534 - val_acc: 0.0113 - val_mean_squared_error: 0.3534 - val_mean_absolute_error: 0.5922 - val_mean_absolute_percentage_error: 4521944.6048 - val_cosine_proximity: -0.9887\n",
      "Epoch 3/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.3671 - acc: 0.0000e+00 - mean_squared_error: 0.3671 - mean_absolute_error: 0.6048 - mean_absolute_percentage_error: 0.2272 - cosine_proximity: -1.000 - ETA: 0s - loss: 79.6318 - acc: 0.0053 - mean_squared_error: 79.6318 - mean_absolute_error: 7.5557 - mean_absolute_percentage_error: 2170927.2957 - cosine_proximity: -0.994 - 0s 375us/step - loss: 51.0748 - acc: 0.0113 - mean_squared_error: 51.0748 - mean_absolute_error: 5.7386 - mean_absolute_percentage_error: 4560838.7139 - cosine_proximity: -0.9887 - val_loss: 9.9712 - val_acc: 0.0113 - val_mean_squared_error: 9.9712 - val_mean_absolute_error: 3.1438 - val_mean_absolute_percentage_error: 4568846.7083 - val_cosine_proximity: -0.9887\n",
      "Epoch 4/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 10.1601 - acc: 0.0000e+00 - mean_squared_error: 10.1601 - mean_absolute_error: 3.1873 - mean_absolute_percentage_error: 1.1974 - cosine_proximity: -1.000 - ETA: 0s - loss: 2.1394 - acc: 0.0208 - mean_squared_error: 2.1394 - mean_absolute_error: 1.1974 - mean_absolute_percentage_error: 1661217.7644 - cosine_proximity: -0.995 - 0s 321us/step - loss: 1.5569 - acc: 0.0226 - mean_squared_error: 1.5569 - mean_absolute_error: 0.9733 - mean_absolute_percentage_error: 4523617.7400 - cosine_proximity: -0.9887 - val_loss: 0.1025 - val_acc: 0.0311 - val_mean_squared_error: 0.1025 - val_mean_absolute_error: 0.3166 - val_mean_absolute_percentage_error: 4518935.6986 - val_cosine_proximity: -0.9887\n",
      "Epoch 5/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0949 - acc: 0.1000 - mean_squared_error: 0.0949 - mean_absolute_error: 0.3059 - mean_absolute_percentage_error: 0.1149 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0656 - acc: 0.0286 - mean_squared_error: 0.0656 - mean_absolute_error: 0.2229 - mean_absolute_percentage_error: 1906362.7496 - cosine_proximity: -0.995 - 0s 363us/step - loss: 0.0439 - acc: 0.0311 - mean_squared_error: 0.0439 - mean_absolute_error: 0.1678 - mean_absolute_percentage_error: 4522297.1210 - cosine_proximity: -0.9887 - val_loss: 0.0049 - val_acc: 0.0311 - val_mean_squared_error: 0.0049 - val_mean_absolute_error: 0.0472 - val_mean_absolute_percentage_error: 4523411.5398 - val_cosine_proximity: -0.9887\n",
      "Epoch 6/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0029 - acc: 0.1000 - mean_squared_error: 0.0029 - mean_absolute_error: 0.0480 - mean_absolute_percentage_error: 0.0181 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0048 - acc: 0.0286 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0503 - mean_absolute_percentage_error: 1906094.1132 - cosine_proximity: -0.995 - 0s 344us/step - loss: 0.0050 - acc: 0.0311 - mean_squared_error: 0.0050 - mean_absolute_error: 0.0463 - mean_absolute_percentage_error: 4522717.6416 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0391 - val_mean_absolute_percentage_error: 4522354.8140 - val_cosine_proximity: -0.9887\n",
      "Epoch 7/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0013 - acc: 0.1000 - mean_squared_error: 0.0013 - mean_absolute_error: 0.0268 - mean_absolute_percentage_error: 0.0101 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0031 - acc: 0.0348 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0371 - mean_absolute_percentage_error: 1740120.5349 - cosine_proximity: -0.995 - 0s 362us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0390 - mean_absolute_percentage_error: 4522012.4411 - cosine_proximity: -0.9887 - val_loss: 0.0040 - val_acc: 0.0311 - val_mean_squared_error: 0.0040 - val_mean_absolute_error: 0.0388 - val_mean_absolute_percentage_error: 4521910.5201 - val_cosine_proximity: -0.9887\n",
      "Epoch 8/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0015 - acc: 0.1000 - mean_squared_error: 0.0015 - mean_absolute_error: 0.0308 - mean_absolute_percentage_error: 0.0116 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0031 - acc: 0.0348 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0376 - mean_absolute_percentage_error: 1739862.1003 - cosine_proximity: -0.995 - 0s 357us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0393 - mean_absolute_percentage_error: 4521379.5599 - cosine_proximity: -0.9887 - val_loss: 0.0040 - val_acc: 0.0311 - val_mean_squared_error: 0.0040 - val_mean_absolute_error: 0.0388 - val_mean_absolute_percentage_error: 4521262.3845 - val_cosine_proximity: -0.9887\n",
      "Epoch 9/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0015 - acc: 0.1000 - mean_squared_error: 0.0015 - mean_absolute_error: 0.0306 - mean_absolute_percentage_error: 0.0115 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0031 - acc: 0.0308 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0375 - mean_absolute_percentage_error: 1538884.1672 - cosine_proximity: -0.996 - 0s 317us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0394 - mean_absolute_percentage_error: 4520708.8254 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0389 - val_mean_absolute_percentage_error: 4520595.7179 - val_cosine_proximity: -0.9887\n",
      "Epoch 10/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0015 - acc: 0.1000 - mean_squared_error: 0.0015 - mean_absolute_error: 0.0315 - mean_absolute_percentage_error: 0.0118 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0031 - acc: 0.0263 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0371 - mean_absolute_percentage_error: 2105504.6445 - cosine_proximity: -0.994 - 0s 383us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0395 - mean_absolute_percentage_error: 4519987.0175 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0390 - val_mean_absolute_percentage_error: 4519883.4016 - val_cosine_proximity: -0.9887\n",
      "Epoch 11/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0016 - acc: 0.1000 - mean_squared_error: 0.0016 - mean_absolute_error: 0.0325 - mean_absolute_percentage_error: 0.0122 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0032 - acc: 0.0333 - mean_squared_error: 0.0032 - mean_absolute_error: 0.0383 - mean_absolute_percentage_error: 1666575.0135 - cosine_proximity: -0.995 - 0s 319us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0396 - mean_absolute_percentage_error: 4519222.8368 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0392 - val_mean_absolute_percentage_error: 4519124.9835 - val_cosine_proximity: -0.9887\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0016 - acc: 0.1000 - mean_squared_error: 0.0016 - mean_absolute_error: 0.0334 - mean_absolute_percentage_error: 0.0126 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0032 - acc: 0.0364 - mean_squared_error: 0.0032 - mean_absolute_error: 0.0385 - mean_absolute_percentage_error: 1817756.7408 - cosine_proximity: -0.995 - 0s 333us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0398 - mean_absolute_percentage_error: 4518417.6391 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0394 - val_mean_absolute_percentage_error: 4518320.4638 - val_cosine_proximity: -0.9887\n",
      "Epoch 13/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0017 - acc: 0.1000 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0342 - mean_absolute_percentage_error: 0.0129 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0033 - acc: 0.0286 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0387 - mean_absolute_percentage_error: 1903959.4422 - cosine_proximity: -0.995 - 0s 369us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0399 - mean_absolute_percentage_error: 4517573.6843 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0396 - val_mean_absolute_percentage_error: 4517465.7746 - val_cosine_proximity: -0.9887\n",
      "Epoch 14/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0017 - acc: 0.1000 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0348 - mean_absolute_percentage_error: 0.0131 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0033 - acc: 0.0286 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0388 - mean_absolute_percentage_error: 1903588.0136 - cosine_proximity: -0.995 - 0s 379us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0399 - mean_absolute_percentage_error: 4516692.3284 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0397 - val_mean_absolute_percentage_error: 4516561.3679 - val_cosine_proximity: -0.9887\n",
      "Epoch 15/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0017 - acc: 0.1000 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0351 - mean_absolute_percentage_error: 0.0132 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0031 - acc: 0.0348 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0379 - mean_absolute_percentage_error: 1737706.6221 - cosine_proximity: -0.995 - 0s 327us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0400 - mean_absolute_percentage_error: 4515773.5713 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0396 - val_mean_absolute_percentage_error: 4515604.5317 - val_cosine_proximity: -0.9887\n",
      "Epoch 16/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0017 - acc: 0.1000 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0349 - mean_absolute_percentage_error: 0.0131 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0032 - acc: 0.0320 - mean_squared_error: 0.0032 - mean_absolute_error: 0.0385 - mean_absolute_percentage_error: 1598354.0937 - cosine_proximity: -0.996 - 0s 318us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0399 - mean_absolute_percentage_error: 4514814.9272 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0395 - val_mean_absolute_percentage_error: 4514597.5260 - val_cosine_proximity: -0.9887\n",
      "Epoch 17/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0017 - acc: 0.1000 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0344 - mean_absolute_percentage_error: 0.0129 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0032 - acc: 0.0320 - mean_squared_error: 0.0032 - mean_absolute_error: 0.0384 - mean_absolute_percentage_error: 1598005.4536 - cosine_proximity: -0.996 - 0s 344us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0398 - mean_absolute_percentage_error: 4513814.5882 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0393 - val_mean_absolute_percentage_error: 4513543.9666 - val_cosine_proximity: -0.9887\n",
      "Epoch 18/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0016 - acc: 0.1000 - mean_squared_error: 0.0016 - mean_absolute_error: 0.0336 - mean_absolute_percentage_error: 0.0126 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0033 - acc: 0.0286 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0385 - mean_absolute_percentage_error: 1901955.6326 - cosine_proximity: -0.995 - 0s 394us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0397 - mean_absolute_percentage_error: 4512771.3113 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0391 - val_mean_absolute_percentage_error: 4512447.4694 - val_cosine_proximity: -0.9887\n",
      "Epoch 19/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0016 - acc: 0.1000 - mean_squared_error: 0.0016 - mean_absolute_error: 0.0328 - mean_absolute_percentage_error: 0.0123 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0033 - acc: 0.0286 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0384 - mean_absolute_percentage_error: 1901505.5373 - cosine_proximity: -0.995 - 0s 333us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0396 - mean_absolute_percentage_error: 4511683.9666 - cosine_proximity: -0.9887 - val_loss: 0.0041 - val_acc: 0.0311 - val_mean_squared_error: 0.0041 - val_mean_absolute_error: 0.0389 - val_mean_absolute_percentage_error: 4511308.9383 - val_cosine_proximity: -0.9887\n",
      "Epoch 20/20\n",
      "354/354 [==============================] - ETA: 0s - loss: 0.0016 - acc: 0.1000 - mean_squared_error: 0.0016 - mean_absolute_error: 0.0320 - mean_absolute_percentage_error: 0.0120 - cosine_proximity: -1.000 - ETA: 0s - loss: 0.0033 - acc: 0.0286 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0384 - mean_absolute_percentage_error: 1901036.7754 - cosine_proximity: -0.995 - 0s 356us/step - loss: 0.0041 - acc: 0.0311 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0395 - mean_absolute_percentage_error: 4510551.9892 - cosine_proximity: -0.9887 - val_loss: 0.0040 - val_acc: 0.0311 - val_mean_squared_error: 0.0040 - val_mean_absolute_error: 0.0388 - val_mean_absolute_percentage_error: 4510132.4410 - val_cosine_proximity: -0.9887\n"
     ]
    }
   ],
   "source": [
    "history = estimator.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    validation_data=(\n",
    "        x_train,\n",
    "        y_train),\n",
    "    epochs=use_epochs,\n",
    "    batch_size=use_batch_size,\n",
    "    shuffle=use_shuffle,\n",
    "    verbose=fit_verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine Error (MSE + MAE + MAPE + CP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 54972.0 MAE: 229.66 MAPE: 1336357.76 CP: -0.99\n"
     ]
    }
   ],
   "source": [
    "mse_err = None\n",
    "mae_err = None\n",
    "mape_err = None\n",
    "cp_err = None\n",
    "if len(history.history['mean_squared_error']) > 0:\n",
    "    mse_err = ae_consts.to_f(\n",
    "        history.history['mean_squared_error'][0])\n",
    "if len(history.history['mean_absolute_error']) > 0:\n",
    "    mae_err = ae_consts.to_f(\n",
    "        history.history['mean_absolute_error'][0])\n",
    "if len(history.history['mean_absolute_percentage_error']) > 0:\n",
    "    mape_err = ae_consts.to_f(\n",
    "        history.history['mean_absolute_percentage_error'][0])\n",
    "if len(history.history['cosine_proximity']) > 0:\n",
    "    cp_err = ae_consts.to_f(\n",
    "        history.history['cosine_proximity'][0])\n",
    "\n",
    "error_str = (\n",
    "    f'MSE: {mse_err} '\n",
    "    f'MAE: {mae_err} '\n",
    "    f'MAPE: {mape_err} '\n",
    "    f'CP: {cp_err}')\n",
    "\n",
    "print(error_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# on production use newly fetched pricing data\n",
    "# not the training data\n",
    "predict_records = df[train_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making predictions: 395\n",
      "395/395 [==============================] - ETA:  - 0s 114us/step\n",
      "historical close with predicted close:                  minute   close  predicted_close\n",
      "5   2019-01-25 09:30:00  265.53           265.58\n",
      "6   2019-01-25 09:31:00  265.36           265.49\n",
      "7   2019-01-25 09:32:00  265.32           265.38\n",
      "8   2019-01-25 09:33:00  265.45           265.42\n",
      "9   2019-01-25 09:34:00  265.32           265.31\n",
      "10  2019-01-25 09:35:00  265.26           265.23\n",
      "11  2019-01-25 09:36:00  265.35           265.29\n",
      "12  2019-01-25 09:37:00  265.22           265.30\n",
      "13  2019-01-25 09:38:00  265.22           265.24\n",
      "14  2019-01-25 09:39:00  265.23           265.25\n",
      "15  2019-01-25 09:40:00  265.39           265.32\n",
      "16  2019-01-25 09:41:00  265.42           265.42\n",
      "17  2019-01-25 09:42:00  265.22           265.23\n",
      "18  2019-01-25 09:43:00  265.34           265.25\n",
      "19  2019-01-25 09:44:00  265.27           265.33\n",
      "20  2019-01-25 09:45:00  265.28           265.33\n",
      "21  2019-01-25 09:46:00  265.32           265.30\n",
      "22  2019-01-25 09:47:00  265.44           265.38\n",
      "23  2019-01-25 09:48:00  265.45           265.51\n",
      "24  2019-01-25 09:49:00  265.42           265.46\n",
      "25  2019-01-25 09:50:00  265.62           265.58\n",
      "26  2019-01-25 09:51:00  265.62           265.62\n",
      "27  2019-01-25 09:52:00  265.73           265.66\n",
      "28  2019-01-25 09:53:00  265.69           265.75\n",
      "29  2019-01-25 09:54:00  265.55           265.64\n",
      "30  2019-01-25 09:55:00  265.43           265.52\n",
      "31  2019-01-25 09:56:00  265.42           265.44\n",
      "32  2019-01-25 09:57:00  265.53           265.47\n",
      "33  2019-01-25 09:58:00  265.58           265.58\n",
      "34  2019-01-25 09:59:00  265.70           265.63\n",
      "..                  ...     ...              ...\n",
      "365 2019-01-25 15:30:00  265.46           265.57\n",
      "366 2019-01-25 15:31:00  265.40           265.39\n",
      "367 2019-01-25 15:32:00  265.38           265.40\n",
      "368 2019-01-25 15:33:00  265.43           265.39\n",
      "369 2019-01-25 15:34:00  265.44           265.44\n",
      "370 2019-01-25 15:35:00  265.36           265.40\n",
      "371 2019-01-25 15:36:00  265.56           265.44\n",
      "372 2019-01-25 15:37:00  265.60           265.58\n",
      "373 2019-01-25 15:38:00  265.56           265.59\n",
      "374 2019-01-25 15:39:00  265.67           265.62\n",
      "375 2019-01-25 15:40:00  265.71           265.65\n",
      "376 2019-01-25 15:41:00  265.75           265.72\n",
      "377 2019-01-25 15:42:00  265.76           265.72\n",
      "378 2019-01-25 15:43:00  265.79           265.76\n",
      "379 2019-01-25 15:44:00  265.77           265.83\n",
      "380 2019-01-25 15:45:00  265.86           265.86\n",
      "381 2019-01-25 15:46:00  265.80           265.84\n",
      "382 2019-01-25 15:47:00  265.82           265.81\n",
      "383 2019-01-25 15:48:00  265.81           265.81\n",
      "384 2019-01-25 15:49:00  265.83           265.81\n",
      "385 2019-01-25 15:50:00  265.77           265.81\n",
      "386 2019-01-25 15:51:00  265.65           265.75\n",
      "387 2019-01-25 15:52:00  265.69           265.66\n",
      "388 2019-01-25 15:53:00  265.56           265.64\n",
      "389 2019-01-25 15:54:00  265.58           265.58\n",
      "390 2019-01-25 15:55:00  265.72           265.67\n",
      "391 2019-01-25 15:56:00  265.76           265.73\n",
      "392 2019-01-25 15:57:00  265.77           265.76\n",
      "393 2019-01-25 15:58:00  265.70           265.75\n",
      "394 2019-01-25 15:59:00  265.80           265.71\n",
      "\n",
      "[389 rows x 3 columns]\n",
      "Dates: 2019-01-25 09:30:00 to 2019-01-25 15:59:00\n",
      "Columns: ['minute', 'close', 'predicted_close', 'error']\n",
      "Average historical close vs predicted close error: -0.01\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'making predictions: {len(predict_records)}')\n",
    "\n",
    "predictions = estimator.model.predict(\n",
    "    predict_records,\n",
    "    verbose=True)\n",
    "\n",
    "np.set_printoptions(threshold=np.nan)\n",
    "indexes = tf.argmax(predictions, axis=1)\n",
    "data = {}\n",
    "data['indexes'] = indexes\n",
    "price_predictions = [ae_consts.to_f(x[0]) for x in predictions]\n",
    "\n",
    "df['predicted_close'] = price_predictions\n",
    "df['error'] = (\n",
    "    df['close'] -\n",
    "    df['predicted_close'])\n",
    "average_error = ae_consts.to_f(\n",
    "    df['error'].sum() / len(df.index))\n",
    "\n",
    "output_features = [\n",
    "    'minute',\n",
    "    'close',\n",
    "    'predicted_close',\n",
    "    'error'\n",
    "]\n",
    "\n",
    "df_filter = (df['close'] >= 0.1)\n",
    "date_str = (\n",
    "    f'Dates: {df[df_filter][\"minute\"].iloc[0]} '\n",
    "    f'to '\n",
    "    f'{df[df_filter][\"minute\"].iloc[-1]}')\n",
    "\n",
    "print(\n",
    "    f'historical close with predicted close: '\n",
    "    f'{df[df_filter][[\"minute\", \"close\", \"predicted_close\"]]}')\n",
    "print(\n",
    "    date_str)\n",
    "print(\n",
    "    f'Columns: {output_features}')\n",
    "print(\n",
    "    f'Average historical close '\n",
    "    f'vs predicted close error: '\n",
    "    f'{average_error}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Historical Close vs DNN's Predicted Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting historical close vs predicted close\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\n",
    "    f'plotting historical close vs predicted close')\n",
    "\n",
    "plot_res = plot_trading_history.plot_trading_history(\n",
    "    title=(\n",
    "        f'{ticker} - Historical Close vs Predicted Close\\n'\n",
    "        f'Error Metrics: {error_str}\\n'\n",
    "        f'{date_str}'),\n",
    "    df=copy.deepcopy(df),\n",
    "    red='close',\n",
    "    blue='predicted_close',\n",
    "    green=None,\n",
    "    orange=None,\n",
    "    date_col='minute',\n",
    "    date_format='%d %H:%M:%S\\n%b',\n",
    "    xlabel='minute',\n",
    "    ylabel='Historical Close vs Predicted Close',\n",
    "    df_filter=df_filter,\n",
    "    width=8.0,\n",
    "    height=8.0,\n",
    "    show_plot=True,\n",
    "    dropna_for_all=False)"
   ]
  }
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